Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields.
Low-frequency oscillations shape how neurons sample their synaptic inputs, hence regulating information exchange among neural circuits. In the hippocampus, theta oscillations (4-8 Hz) enable the temporal organization of cortical inputs, resulting in a phase code. However, the advantages of the specific theta band over other frequency ranges remain unclear. It is possible that this specific frequency range is optimizing a trade-off between information throughput and biophysical constraints of the neuronal substrate. To test this hypothesis, we analyze a physiologically constrained model of the rodent hippocampus comprising stochastic leaky integrate-and-fire neurons driven by oscillations. By estimating the information rate of the phase code for a range of noise and frequency levels, we identify a trade-off between sampling frequency and coding precision. We observe that, under physiological noise levels, theta-band oscillations optimize the speed-precision trade-off, maximizing information rate. Further, we demonstrate that theta optimizes this trade-off throughout the entire dorsoventral axis of the hippocampus with its pronounced gradients of several physiological properties. In addition, we find that maintaining an optimal information rate relies on the concurrent modulation of both frequency and amplitude, hence explaining the locomotion speed-dependent modulations of the hippocampal theta oscillation that are observed in rodents and humans. Overall, our results suggest that low-frequency oscillations are adapted to maximize the information rate of neural sampling given the temporal and biophysical constraints under which neural circuits operate.
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